Introduction
Welcome to FLYDATA, where we transform aviation data into actionable insights. This demonstration showcases our analytical prowess with an exploration of the nycflights23 dataset, capturing New York City flights in 2013. Our capabilities allow us to tackle questions such as:
- What is the average, median, and standard deviation for departure delay, arrival delay, and air time?
- What the number of flights for each airline?
- Are there seasonal trend (e.g. number of flights per month)?
- How high are flight departure delays throughout the day?
- What is the relationship between air time and distance?
- Which routes observe the most flights, and how can this inform operational decisions?
- How do weather conditions, particularly at key hubs, impact flight schedules?
- Are certain days more prone to operational disruptions, and how can this optimize staffing?
- What insights can be derived from analyzing aircraft age in relation to flight frequency?
- What factors influence customer satisfaction?
1. Descriptive Statistics
Our Capabilities
At FLYDATA, we specialize in transforming raw aviation data into actionable insights that empower our customers to optimize their operations and enhance customer satisfaction. By providing critical statistics such as the average, median, and standard deviation for departure delay, arrival delay, and air time, we enable our clients to gain a comprehensive understanding of their flight punctuality and operational efficiency.
Analysis
# Summarize the mean, median, and standard deviation for departure delay, arrival delay, and air time
# Use what you learned about the six verbes and display your summary as a table :)
flights_summary <- flights |>
reframe(
averages = c(mean(dep_delay, na.rm = TRUE),
mean(arr_delay, na.rm = TRUE),
mean(air_time, na.rm = TRUE)),
median = c(median(dep_delay, na.rm = TRUE),
median(arr_delay, na.rm = TRUE),
median(air_time, na.rm = TRUE)),
standard_deviation = c(sd(dep_delay, na.rm = TRUE),
sd(arr_delay, na.rm = TRUE),
sd(air_time, na.rm = TRUE))
) |> mutate(variable = c("Departure Delay","Arrival Delay","Airtime"))
# Print the summary
#print(flights_summary)
# Print the summary (in a nicer looking way)
kable(flights_summary, "html") |>
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = F)| averages | median | standard_deviation | variable |
|---|---|---|---|
| 13.837372 | -2 | 54.31385 | Departure Delay |
| 4.344803 | -10 | 57.86889 | Arrival Delay |
| 141.820258 | 121 | 89.17256 | Airtime |
2. Number of Flights Analysis
Capabilities
Knowing the total flight count for each airline provides our clients with crucial information to assess market share, identify competitive strengths, and spot potential opportunities for collaboration or strategic alliances. This data allows airlines to benchmark themselves against competitors, evaluate their fleet utilization, and optimize route planning.
Analysis
flights_by_airline <- flights |>
group_by(carrier) |>
summarize(number_of_flights = n()) |>
arrange(desc(number_of_flights))
# Join with airlines for full name and print
flights_by_airline <- flights_by_airline |>
left_join(airlines, by = "carrier")
# Print the summary
# print(flights_by_airline)
# Print the summary (in a nicer looking way)
kable(flights_by_airline, "html") |>
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = F)| carrier | number_of_flights | name |
|---|---|---|
| YX | 88785 | Republic Airline |
| UA | 79641 | United Air Lines Inc. |
| B6 | 66169 | JetBlue Airways |
| DL | 61562 | Delta Air Lines Inc. |
| 9E | 54141 | Endeavor Air Inc. |
| AA | 40525 | American Airlines Inc. |
| NK | 15189 | Spirit Air Lines |
| WN | 12385 | Southwest Airlines Co. |
| AS | 7843 | Alaska Airlines Inc. |
| OO | 6432 | SkyWest Airlines Inc. |
| F9 | 1286 | Frontier Airlines Inc. |
| G4 | 671 | Allegiant Air |
| HA | 366 | Hawaiian Airlines Inc. |
| MQ | 357 | Envoy Air |
3. Seasonal Trends
Our Capabilities
We harness the power of data analytics to uncover seasonal trends that are pivotal for strategic planning and operational efficiency in the aviation industry. Analyzing the number of flights per month allows us to identify seasonal patterns that have significant implications for airlines and airports alike.
Analysis
flights_by_month <- flights |>
mutate(
month = case_when(
month == 1 ~ "Januar",
month == 2 ~ "Februar",
month == 3 ~ "März",
month == 4 ~ "April",
month == 5 ~ "Mai",
month == 6 ~ "Juni",
month == 7 ~ "Juli",
month == 8 ~ "August",
month == 9 ~ "September",
month == 10 ~ "Oktober",
month == 11 ~ "November",
month == 12 ~ "Dezember",
TRUE ~ as.character(month) # to handle unexpected values, though ideally shouldn't occur
),
month = factor(month, levels = c("Januar", "Februar", "März", "April", "Mai", "Juni",
"Juli", "August", "September", "Oktober", "November", "Dezember"))
) |>
group_by(month) |>
summarize(number_of_flights = n())
# Plotting flights per month
flights_by_month_plot <- flights_by_month |>
ggplot(aes(x = month, y = number_of_flights)) +
geom_col(fill = "#272727") +
labs(title = "Number of Flights per Month", x = "Month", y = "Number of Flights") +
theme_minimal()
ggplotly(flights_by_month_plot)4. Departure and Arrival Delays throughout the Day
Our Capabilities
By providing insights into these patterns, we help airlines and airports optimize their schedules, enhance ground operations, and minimize delays. This data-driven approach not only enhances operational effectiveness but also significantly improves passenger experience by reducing wait times and maintaining reliable schedules.
Analysis
delay_by_hour <- flights |>
group_by(hour) |>
summarize(avg_dep_delay = mean(dep_delay, na.rm = TRUE),
avg_arr_delay = mean(arr_delay, na.rm = TRUE))
# Plotting average departure delay per hour
delay_by_hour |>
ggplot() +
geom_line(aes(x = hour, y = avg_dep_delay, color = "Departure"), linewidth = 1) +
geom_line(aes(x = hour, y = avg_arr_delay, color = "Arrival"), linewidth = 1) +
labs(
title = "Average Delay by Hour",
x = "Hour of Day",
y = "Average Delay (minutes)",
color = "Type"
) +
theme_minimal() -> delay_by_hour_plot
ggplotly(delay_by_hour_plot)5. The Relationship between Airtime and Distance?
Our Capabilities
By offering a detailed analysis of the air time and distance relationship, we enable airlines to optimize route planning, improve flight scheduling, and enhance overall operational performance, ensuring a balance between efficiency, cost, and customer satisfaction.
Analysis
air_time_distance_plot <- flights |>
ggplot(aes(x = distance, y = air_time)) +
geom_point(alpha = 0.3, color = "blue") +
geom_smooth(method = "lm", col = "red") +
labs(title = "Air Time vs. Distance", x = "Distance (miles)", y = "Air Time (minutes)") +
theme_light()
air_time_distance_plot## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 12534 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 12534 rows containing missing values or values outside the scale range
## (`geom_point()`).
6. Busiest Routes
Question
FLYDATA begins by identifying the busiest routes. Insights into high-traffic corridors can drive strategic planning for airlines and airports.
Analysis
busiest_routes <- flights |>
group_by(origin, dest) |>
summarize(number_of_flights = n()) |>
arrange(desc(number_of_flights)) |>
ungroup()## `summarise()` has grouped output by 'origin'. You can override using the
## `.groups` argument.knitr::kable(head(busiest_routes, 10), caption = "Top 10 Busiest Routes from NYC")| origin | dest | number_of_flights |
|---|---|---|
| JFK | LAX | 10045 |
| LGA | ORD | 9923 |
| LGA | BOS | 8217 |
| LGA | ATL | 7883 |
| JFK | SFO | 7440 |
| EWR | MCO | 7262 |
| JFK | BOS | 6432 |
| LGA | DFW | 5972 |
| JFK | MIA | 5930 |
| EWR | ATL | 5915 |
Visualization
busiest_routes |>
top_n(10, number_of_flights) |>
ggplot(aes(x = reorder(paste(origin, dest, sep = " - "), number_of_flights), y = number_of_flights)) +
geom_bar(stat = "identity", fill = "lightgreen") +
coord_flip() +
labs(title = "Top 10 Busiest Routes from NYC", x = "Route", y = "Number of Flights") +
theme_minimal() -> busiest_routes_plot
ggplotly(busiest_routes_plot)Findings
Our exploration identifies key routes to major airports such as LAX, ORD, and ATL. This information aids in optimizing fleet allocation and scheduling.
7. Weather Impact on Delays
Question
Analyzing the influence of weather, especially at JFK, FLYDATA assesses its impact on delays to bolster operational resilience.
Analysis
jfk_weather_delay <- flights |>
filter(origin == "JFK") |>
left_join(weather, by = c("origin", "year", "month", "day", "hour")) |>
group_by(date = as.Date(paste(year, month, day, sep = "-"))) |>
summarize(avg_dep_delay = mean(dep_delay, na.rm = TRUE),
avg_wind_speed = mean(wind_speed, na.rm = TRUE))
knitr::kable(head(jfk_weather_delay), caption = "Sample Data of JFK Wind Speed Impact on Delays")| date | avg_dep_delay | avg_wind_speed |
|---|---|---|
| 2023-01-01 | 18.764045 | 10.874225 |
| 2023-01-02 | 45.703833 | 7.068520 |
| 2023-01-03 | 38.898649 | 4.901329 |
| 2023-01-04 | 32.215488 | 5.684622 |
| 2023-01-05 | 11.787879 | 6.163238 |
| 2023-01-06 | 7.862876 | 5.199678 |
Visualization
jfk_weather_delay |>
ggplot(aes(x = avg_wind_speed, y = avg_dep_delay)) +
geom_point(alpha = 0.5, color = "orange") +
geom_smooth(method = "lm", col = "darkgreen") +
labs(title = "Impact of Wind Speed on Departure Delay at JFK", x = "Average Wind Speed (miles/hour)", y = "Average Departure Delay (minutes)") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
Findings
Identifying a positive correlation, we recommend leveraging weather data to anticipate delays and refine scheduling protocols.
8. Delays by Day of the Week
Question
FLYDATA investigates whether certain days endure more delays, thus suggesting staffing and scheduling efficiency opportunities.
Analysis
flights_dayofweek <- flights |>
mutate(weekday = weekdays(as.Date(paste(year, month, day, sep = "-")))) |>
group_by(weekday) |>
summarize(avg_dep_delay = mean(dep_delay, na.rm = TRUE)) |>
arrange(match(weekday, c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")))
knitr::kable(flights_dayofweek, caption = "Average Departure Delay by Day of the Week")| weekday | avg_dep_delay |
|---|---|
| Monday | 14.73471 |
| Tuesday | 10.85576 |
| Wednesday | 10.60587 |
| Thursday | 11.90029 |
| Friday | 16.58209 |
| Saturday | 15.84804 |
| Sunday | 16.99358 |
Visualization
flights_dayofweek |>
ggplot(aes(x = weekday, y = avg_dep_delay)) +
geom_bar(stat = "identity", fill = "dodgerblue") +
labs(title = "Average Departure Delay by Day of the Week", x = "Day of Week", y = "Average Departure Delay (minutes)") +
theme_minimal()
Findings
Delays are prevalent on weekends, suggesting a strategic focus on weekends for staffing and resource allocation.
9. Aircraft Age Analysis
Question
Our analysis examines the relationship between aircraft age and usage, providing insights into fleet management strategies.
Analysis
plane_ages <- flights |>
left_join(planes, by = "tailnum") |>
mutate(plane_age = 2023 - year.y) |>
group_by(plane_age) |>
summarize(number_of_flights = n())
knitr::kable(filter(plane_ages, !is.na(plane_age)), caption = "Flights by Aircraft Age in 2013")| plane_age | number_of_flights |
|---|---|
| 0 | 9004 |
| 1 | 15393 |
| 2 | 10007 |
| 3 | 9323 |
| 4 | 14729 |
| 5 | 11362 |
| 6 | 20087 |
| 7 | 13653 |
| 8 | 19259 |
| 9 | 32598 |
| 10 | 24685 |
| 11 | 7089 |
| 12 | 5109 |
| 13 | 7061 |
| 14 | 11313 |
| 15 | 44442 |
| 16 | 30558 |
| 17 | 16239 |
| 18 | 15511 |
| 19 | 9622 |
| 20 | 5580 |
| 21 | 9691 |
| 22 | 15163 |
| 23 | 16022 |
| 24 | 15825 |
| 25 | 11340 |
| 26 | 3733 |
| 27 | 1801 |
| 28 | 1704 |
| 29 | 3494 |
| 30 | 993 |
| 31 | 1455 |
| 32 | 1243 |
| 33 | 1204 |
Visualization
plane_ages |>
filter(!is.na(plane_age)) |>
ggplot(aes(x = plane_age, y = number_of_flights)) +
geom_col(fill = "coral") +
labs(title = "Flights by Aircraft Age in 2013", x = "Aircraft Age (years)", y = "Number of Flights") +
theme_minimal()
Findings
Our findings reveal diverse aircraft usage across various ages, informing maintenance and fleet strategies to enhance safety and cost-efficiency.
10. Influences on Customer Satisfaction
Question
Customer satisfaction is a multidimensional aspect that’s pivotal for the sustained success and reputation of airlines and airports. At FLYDATA, we analyze various factors influencing customer satisfaction to provide our clients with insights for enhancing passenger experiences and ensuring customer loyalty. Here’s a look at some key insights into customer satisfaction:
Analysis
Visualisation
Conclusion
Wind-Up!
This demonstration from FLYDATA illustrates how our data-driven insights can shape operational excellence in the aviation industry. From understanding route dynamics to optimizing aircraft usage, FLYDATA empowers stakeholders to make informed decisions. For bespoke analysis and deeper dives into your specific needs, connect with our team to explore customized solutions.